According to a 2019 World Economic Forum and McKinsey study, while 70% of companies believe Industry 4.0 is critical to their future, only 15% have a plan.
So, why the disconnect?
Because most don’t know where to start. The idea of using artificially intelligent and integrated systems to control your plant and warehouse sounds daunting and unapproachable, even scary. Who’s going to architect the solutions? Implement and maintain them? Secure them?
Digital transformation in manufacturing — including Industry 4.0 implementation — doesn’t have to be overwhelming. You need a deliberate approach that breaks this journey down into manageable stages.
First, build a strong digital twin foundation that’s adaptable to an unknown (even unknowable) future state. Second, implement a proof-of-concept for an easy win. Third, take a bigger leap; one that’s capable of delivering significant value. Fourth, continuously enhance, optimize, and improve.
As we discuss in our Complete Digital Transformation Planning and ERP Selection Guide, a key to success is building a strategically-aligned technology plan — one that clearly maps technology drivers to measurable business outcomes.
In these uncertain times, companies are being forced to accelerate the adoption of automation and of integrated technologies that allow them to rapidly shift supply chains, respond to volatile demand shifts, and operate with reduced or mobile workforces.
In this article, we break down the keys to digital transformation:
- A primer on Industry 4.0 — what it is and why it’s important
- The history of Industry 4.0 — what brought it about and why it’s revolutionary
- 4 pillars of Industry 4.0 — the underpinnings of manufacturing digital transformation
- 6 critical steps to delivering digital transformation success
- 2 case studies of manufacturers that are using Industry 4.0 to digitally transform for the future
- Breaking down 3-tiered Industry 4.0 architecture: cloud, fog, and edge computing
What is Industry 4.0?
Industry 4.0 is an opportunity to shift low-value tasks to systems and machines. It’s an opportunity to democratize decision-making based on the availability of timely analysis. It’s an opportunity to make better use of key assets: people.
The term “Industry 4.0”— or the Fourth Industrial Revolution — refers to the transformation of traditional manufacturing by deeply connecting manufacturing and back-office processes with smart technologies. More technically, it refers to self-optimizing cyber-physical industrial systems. In these environments, systems collect data, create analytical models, make decisions, and optimize production. It’s referred to as a revolution because of the potential to impact our socio-economic fabric to the same extent as the steam engine.
For example, the first industrial revolution was born of innovations relating to steam power, leading to rapid factory development and associated production efficiencies. This productivity gain supported growing consumerism, urbanization, education, employment, and, in short, capitalism.
The second industrial revolution reflected widespread industrialization driven by mass production, steel and ironworks, electrification, and widespread rail transport adoption. These innovations led to modern business management practices and integrated supply chains, increasing division of labor into skilled and unskilled categories, and for better or worse, widespread adoption of tariffs to protect national economies.
The third industrial revolution — the digital revolution — followed advances in semiconductor technologies that enabled personal computing, digital record keeping, cellular phone technologies, and the internet. The result was interconnectedness, globalization, and business models such as outsourcing and e-commerce.
Building upon its predecessors, the latest, fourth revolutionary iteration is catalyzed by cloud computing, the Industrial Internet of Things (IIoT), and Big Data. Cloud enables the economical storage of large datasets. Analytics and artificial intelligence (AI) rapidly analyze those large datasets, uncover new relationships, and surface new insights. Insights provide decision-makers with timely and relevant decision-supporting information, as well as optimized, connected operations.
The History of Industry 4.0 and Digital Transformation
Industry 4.0 was conceived as part of a German government strategic initiative to computerize manufacturing with the goal of protecting the country’s position as a manufacturing powerhouse.
In April of 2011, a working group presented a draft white paper with the thesis that Germany can continue to thrive as a manufacturing hub, notwithstanding its high-wage economy. The authors proposed a framework based on smart, cyber-physical systems that connect equipment, software, and people. The final report was presented at Hannover Messe in April of 2013.
How Can a Company Use Digital Transformation and Industry 4.0 to Improve Its Competitive Position?
The following client case study discusses how a company will be using self-optimizing systems to improve its competitive positioning.
Our client’s primary product is a grain-based fuel. Through its bulk production process, the company produces various coproducts and byproducts. Coproduct and byproduct yield vary relative to primary product yield. And, all yield varies based on production parameters and envinronmental conditions relating to speed, humidity, temperature, vibration, weather conditions, system pressures, and raw material grades.
To truly optimize for profit, production systems would need continuous adjustment. We modeled two scenarios: one using people and another using connected systems and equipment.
The person-powered scenario wouldn’t deliver a positive ROI. The incremental cost of employees exceeded the incremental profit margin benefits associated with yield improvements. There would also be execution timing issues. It would simply take too long to run the calculations and manually adjust production equipment controls. We needed the systems to respond more quickly than humanly possible.
We then modeled another scenario, one where software systems crunch the data and make micro changes to production processing controls. The business case was supportive. While initial technology acquisition and implementation costs would be high, subsequent costs to maintain and optimize the system would be low, and certainly lower than the annually recurring salary costs in the first scenario.
The model demonstrated a payback within a few years, along with a significant return-on-investment over a 10-year forecast period. We’re in the stages of implementing the program. To get ready, we’re redesigning the company’s organizational structure, rearchitecting critical data models, and implementing foundational systems.
The Four Pillars of Industry 4.0
Industry 4.0 is the next-generation smart framework we’re implementing at our fuel client. If we reflect on the evolution of business technologies, the central theme has always been to automate and integrate processes to eliminate or at least minimize transactional and decision-making impediments. Now, we’re building systems capable of making and acting on ‘intelligent’ decisions.
In the 1960s, MRP (materials requirements planning) sought to remove friction between sales and operations by time-phasing purchase and manufacturing material requirements with demand for those resources. ERP (enterprise resource planning) came next. This innovation removed finance and accounting frictions by integrating the operational transactions to the general ledger, accounts payable, and accounts receivable modules.
Today, we’re seeing previously siloed manufacturing control and execution systems being integrated with enterprise software systems — systems that drive the planning of production, equipment maintenance, quality, and inventory processes.
What makes these integrated systems “Industry 4.0” is the layering of artificial intelligence that learns from data and drives autonomous decisions to systematically optimize operations.
When designing your organization’s Industry 4.0 environment, you need to consider four overlapping principles, according to the authors of “Design Principles for Industrie 4.0 Scenarios”:
- Interconnection. The systems need to connect people, machines, sensors, devices, and software through the Internet of Things (IoT) and allow them to communicate with one another.
- Information transparency. The data collected through interconnection needs to be made available to operators for decision-making.
- Technical assistance. The intent is twofold: 1) to shift low-value tasks from people to cyber-physical systems, and 2) for systems to provide people with information to make timely and effective decisions.
- Decentralized decisions. The systems need to be able to make their own decisions and take autonomous action.
Returning to our above case study, our client first needs to build its digital twin, which refers to a digital clone of the physical operations. This requires sensors and the Internet of Things (IoT) to connect machines, scales, tanks, controllers, and other equipment with the cyber-world. The data from all of those systems would need to be collected in a data lake (a store of big and oftentimes unstructured datasets), which feeds a cognitive analytics engine that both brings timely insights to the operators and drives autonomous process controls.
For example, when the system’s artificial intelligence “brain” discovers a higher profit mix of primary and co-product, it needs to be able to automatically adjust production.
As you imagine your future world, “where should I start?” is probably the toughest question you’re going to have to answer.
How to Approach Industry 4.0 and Digital Transformation
Any Industry 4.0 project requires a foundation that includes a digital twin and enabling organizational structure, data, processes, and technologies. We recommend a six-stage approach.
Six-stage Path to Approach Industry 4.0
- Explore the possibilities: Learn about industry trends, modern technologies, and use cases.
- Build a holistic strategy: Hold a strategic planning session to identify opportunities to enhance competitive advantages and new market opportunities with key technology and business stakeholders.
- Architect the future state: Develop an integrated architecture that covers organizational structures, business processes, data, technologies, and risk management
- Prioritize the projects: First, you need to get your house in order. This means properly implementing ERP and other enterprise applications, networks, and supporting infrastructure. Once you’ve built your foundation, develop a realistic time-phased plan to deliver the future state.
- Plan the projects: Develop detailed project schedules, budgets, and resource plans.
- Execute: Get it done!
Oftentimes, it makes sense to start with a proof-of-concept designed to demonstrate a quick-win and rapid ROI. And, if the proof-of-concept delivers strategic business value, senior leaders will be more supportive of – even champion – larger scale transformative projects.
Our fuel client didn’t have a digital twin foundation. Its cyber world didn’t mirror its physical world. Data was inaccurate, processes were manual, and systems were unintegrated.
As a preliminary step, we needed to build its digital twin by:
- Architecting an environment that spans enterprise resources planning (ERP), manufacturing execution systems (MES), and distributed control systems (DCS) with IIoT, business intelligence and Big Data warehousing.
- Properly implementing those solutions.
- Assuring that the cyber-world mirrors the physical world through system adoption and disciplined business processing.
- From an organizational structure perspective, creating new functions, re-levelling work, and establishing cross-functional accountabilities to support deeply integrated administrative and operational work.
With an integrated foundation in place, our client can continuously implement new changes and improve upon existing transformative Industry 4.0 projects.
From Strategy to Architecture Building – Understanding Cloud, Fog, and Edge
Companies wanting to leverage AI to drive autonomous operations need to think about how the pieces fit together. On one hand, a part of the solution needs high-capacity computing resources for AI decision-making. This type of activity lends itself to cloud computing. On the other hand, another part of the solution needs to be able to act on data almost instantaneously as it controls industrial equipment. These types of extreme low latency needs often require computing to happen near the equipment and away from the cloud.
In the context of Industry 4.0, we think about computing architecture in three tiers: cloud, fog, and edge. What differentiates these tiers is the proximity of computing to the data source and whether that computing is centralized.
Cloud computing is at one end of the spectrum, where broadly sourced data is centrally processed. Companies move computing to the cloud when they want centralized computing horsepower that might otherwise be too expensive or complex to set up and manage themselves.
Edge computing pushes computing application, data, and services to the opposite logical extremes of a network and away from centralized nodes. A company decides to implement edge computing:
- When there is a need for extremely low latency
- Where there are high costs to transfer the data to the cloud
- Where connectivity is an issue
- Where compliance demands local processing
Fog computing is a superset of edge computing that bridges the continuum between cloud and edge. There’s still a need for data-dense, low-latency processing, but there’s also a need to compute centrally across multiple edge solutions.
Pemeco works with a manufacturer that extrudes resins used in a variety of applications — from highly regulated aerospace products to sporting equipment. At its primary production facility, the company was wasting $350,000 annually because of poor product quality. The losses included direct costs of excessive scrap and indirect costs of suboptimal customer service. The company was routinely re-running work orders, causing it to juggle production schedules and miss its promised delivery dates. Customers weren’t happy.
So, we helped our client architect an environment that would allow it to detect and react to quality issues much more quickly. We designed a program intended to reduce unplanned scrap by 80 percent and improve its perfect order index KPI to 90 percent (a weighted formula that accounts for quality, lead times, promise dates, and fill rates).
The technology environment involved implementing and interfacing various information technologies (IT) and operational technologies (OT) that include ERP, a warehouse management system (WMS), manufacturing execution system (MES), laboratory information management system (LIMS), programmable logic controllers (PLCs), production equipment, and artificial intelligence (AI).
The edge-fog-cloud solution proved to be a perfect fit.
At the edge, the company leveraged its existing investments in modern manufacturing equipment control systems.
We architected a new fog computing tier that allowed our client to realize big-time Industry 4.0 benefits by automating, interfacing, and systematizing what were previously manual, inefficient, and costly manufacturing execution and laboratory sampling processes.
In its legacy process, shortly after the start of every work order, an operator would run a product sample to the lab to determine whether the extruders were properly set up to produce products of acceptable tensile strength, melt point, color, and a host of other quality attributes. If the results were outside of customer specifications, machine operators would manually adjust various process controls to get the product within acceptable tolerances.
We designed a system that closed the loop among the LIMS, MES, and the equipment control systems using the Internet of Things (IoT) and application programming interfaces (APIs). When lab-quality results are posted, the results are capable of automatically triggering changes to equipment process controls to yield products at appropriate quality standards — updating parameters such as flow rates, temperatures, and screw RPMs.
The purpose of this fog computing solution is to use these powerful systems for what they’re good at: quick, powerful data analysis, and efficient process execution. The automation is far more efficient and scientific than the previous human operator “thumb-in-the-wind” process of guessing how much to adjust the process controls.
The cloud layer of the architecture supports ERP, machine learning, and business intelligence (BI) applications.
ERP would manage all standard back and front office functions, master scheduling, and MRP. Bi-directional interfaces were designed to release warehouse and production orders from ERP to the warehouse, laboratory, and manufacturing systems. Those systems would close the loop by reporting actuals back to ERP for inventory, costing, financial accounting, customer service, supply chain, and other functions.
Business intelligence was structured to sit atop a data lake into which data from multiple sources would flow.
This three-tiered solution isn’t our client’s end game. Rather, it’s the foundation for its Industry 4.0 program. The company has positioned itself to take advantage of machine learning innovations that its technology vendors are routinely releasing. These innovations will ultimately drive further improvements to product quality, process efficiency, and equipment performance.
How Industry 4.0 Affects Jobs
As with every major industrial change, many worry that Industry 4.0 will destroy jobs through automation. However, if done properly, McKinsey and the World Economic Forum believe that Industry 4.0 should be an “injector of human capital (…) transforming work to make it less repetitive, more interesting, diversified, and productive”.
This makes sense. Companies will start collecting analysis-ready mountains of data. They’re going to need data scientists to manage that data. They’re going to need strategic thinkers to decide how to monetize that data, enter new markets, and implement new business models.
And, they’re also going to need front-line workers who are closest to the business process to make decisions about optimization. Finally, as operations, machines, and software become increasingly connected, companies are going to have to fill new technical, security, and risk management roles.
Organizational models that worked well in the past won’t work in future. They weren’t meant to support Industry 4.0 concepts. Industry 4.0 benefits will not be achievable if a company does not rethink its organizational structure.
Making changes to organizational structures and embedded cultures can be exceedingly difficult. Changes must be well-timed. Don’t prematurely create new roles for data science or anticipate new revenue streams before the underlying structures are ready. Similarly, don’t build out new processes and systems if the business functions, reporting structures, and responsibilities are not in place.
To succeed, we recommend a holistic and integrated business transformation plan – one that coordinates interdependent changes to organizational structures, data, processes, and technologies.
Take the Next Step of Industry 4.0 and Digital Transformation
If you’re reading this and you haven’t yet decided how to adopt Industry 4.0 in your manufacturing business, you’re not alone. Many companies find themselves at the foundational stages of Industry 4.0 — architecting the processes and systems to support a digital twin.
- Plan your digital transformation strategy
- Map your business processes
- Define your business and ERP requirements
- Architect your future state
- Select the right technology vendors
For more information on digital transformation and ERP selection, check out “The Ultimate ERP Selection Guide: Templates, Checklists & Scorecards”, which has powered hundreds of companies to select the right software at each stage.
Accelerate your project by downloading “The Ultimate ERP Selection Guide: Templates, Checklists & Scorecards” here.
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